History of Artificial Intelligence
Introduction to Artificial Intelligence and Machine Learning
Artificial Intelligence (AI) is the simulation of human intelligence in machines
that are programmed to think like humans and mimic their actions.
Machine Learning (ML) is a subset of AI that provides systems the ability to
automatically learn and improve from experience without being explicitly
programmed.
Limitations of Deep Learning
Deep learning algorithms require a large amount of data to train and can be
computationally expensive.
They can also be prone to overfitting, where the model performs well on the
training data but poorly on new, unseen data.
Basic Structure of a Perceptron
A perceptron is the simplest type of artificial neural network.
It consists of a single layer of neurons that take in a set of inputs, apply weights
to them, and pass the weighted sum through an activation function to produce
an output.
House Pricing Index Prediction: Linear Regression
Linear regression is a statistical model that is used to analyze the relationship
between two continuous variables.
It can be used to predict the price of a house based on various features such as
the number of rooms, location, and size.
Linear Regression: Mean Squared Error and Root Mean Squared Error in Model
Accuracy
Mean Squared Error (MSE) is a measure of the average squared difference
between the actual and predicted values.
Root Mean Squared Error (RMSE) is the square root of the MSE and is used to
give the prediction error in the same units as the output variable.
Introduction to Artificial Intelligence and Machine Learning
Artificial Intelligence (AI) is the simulation of human intelligence in machines
that are programmed to think like humans and mimic their actions.
Machine Learning (ML) is a subset of AI that provides systems the ability to
automatically learn and improve from experience without being explicitly
programmed.
Limitations of Deep Learning
Deep learning algorithms require a large amount of data to train and can be
computationally expensive.
They can also be prone to overfitting, where the model performs well on the
training data but poorly on new, unseen data.
Basic Structure of a Perceptron
A perceptron is the simplest type of artificial neural network.
It consists of a single layer of neurons that take in a set of inputs, apply weights
to them, and pass the weighted sum through an activation function to produce
an output.
House Pricing Index Prediction: Linear Regression
Linear regression is a statistical model that is used to analyze the relationship
between two continuous variables.
It can be used to predict the price of a house based on various features such as
the number of rooms, location, and size.
Linear Regression: Mean Squared Error and Root Mean Squared Error in Model
Accuracy
Mean Squared Error (MSE) is a measure of the average squared difference
between the actual and predicted values.
Root Mean Squared Error (RMSE) is the square root of the MSE and is used to
give the prediction error in the same units as the output variable.
